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van Ineveld, R. L.

Publications and source records attributed to van Ineveld, R. L..

3 recordsLinked to original sources

Regional specificity of neuronal cell types, functional connectivity, and cell type-specific correlation to brain disorders in the human ventral tegmental area

The ventral tegmental area (VTA) in the midbrain is a key reward system hub associated with multiple brain disorders. While rodent studies reveal a diverse neuronal population and distinct connectivity patterns, the architecture of the human VTA remains poorly understood. Here, we generated a transcriptomic reference atlas of the adult human VTA with subregional resolution. We identify multiple neuronal cell types defined by differential transcription factor expression, as well as regionally enriched GABAergic cell types. Functional connectivity analysis with fMRI suggests that this spatial organization corresponds to distinct connectivity profiles. Integration with other human midbrain single-nucleus RNA-seq datasets reveals a VTA-specific combinatorial GABA-dopaminergic cell type and regional differences among dopaminergic neurons. Finally, we find multiple associations between neuronal cell types and genetic risk for psychiatric disorders and body mass index. Altogether, this study provides a foundation for mechanistic and therapeutic studies into VTA-related diseases.

neuroscience↗

Single-cell spatial analysis of pediatric high-grade glioma reveals a novel population of SPP1+/GPNMB+ myeloid cells with immunosuppressive and tumor-promoting capabilities

BackgroundPediatric-type diffuse high-grade gliomas (pHGG) are a leading cause of pediatric cancer-related mortality. Although immunotherapy offers a promising treatment avenue, clinical responses in pHGG patients remain limited. A detailed understanding of the tumor immune microenvironment (TIME) is essential for advancing immunotherapeutic strategies. MethodsWe performed single-cell spatial analysis integrating cyclical immunofluorescence imaging and Spatial Molecular Imaging to interrogate the proteomic and transcriptomic landscape of pHGG. A tissue microarray comprising 32 diagnostic patient-derived pHGG samples was utilized to map the spatial distribution of immune and tumor cells. ResultsOur analyses reveal that the pHGG TIME is predominantly composed of myeloid cells, including brain-resident microglia and monocyte-derived macrophages, with only few T cells. A significant subset of these myeloid cells express mesenchymal-like genes and are positive for SPP1 and GPNMB. Spatial mapping further demonstrated that SPP1+/GPNMB+ myeloid cells localize in close proximity to mesenchymal-like tumor cells, and negatively correlate with the location and presence of CD8+ T cells. These cells also express genes related to immunosuppression and epithelial-to-mesenchymal transition, indicating their potential role in establishing an immunosuppressive niche. ConclusionsOur findings reveal a distinct immune landscape in pHGG characterized by SPP1+/GPNMB+ myeloid cells which may contribute to the exclusion of CD8+ T cells. This spatially resolved insight identifies these myeloid cells as promising therapeutic targets and provides a rationale for developing novel immunotherapeutic strategies to improve outcomes in pediatric high-grade gliomas.

cancer biology↗

Behavioral-transcriptomic landscape of engineered T cells targeting human cancer organoids

Cellular immunotherapies are rapidly gaining clinical importance, yet predictive platforms for modeling their mode of action are lacking. Here, we developed a dynamic immuno-organoid 3D imaging-transcriptomics platform; BEHAV3D, to unravel the behavioral and underlying molecular mechanisms of solid tumor targeting. Applied to an emerging cancer metabolome-sensing immunotherapy: TEGs, we first demonstrate targeting of multiple breast cancer subtypes. Live-tracking of over 120,000 TEGs revealed a diverse behavioral landscape and identified a super engager cluster with serial killing capability. Inference of single-cell behavior with transcriptomics identified the gene signature of super engager killer TEGs, which contained 27 genes with no previously described T cell function. Furthermore, guided by a dynamic type 1 interferon (IFN-I) signaling module induced by high TEG-sensitive organoids, we show that IFN-I can prime resistant organoids for TEG-mediated killing. Thus, BEHAV3D characterizes behavioral-phenotypic heterogeneity of cellular immunotherapies and holds promise for improving solid tumor-targeting in a patient-specific manner.

cancer biology↗